An analysis of organism lifelines in an industrial bioreactor using Lattice‐Boltzmann CFD

Abstract

Euler-Lagrange CFD simulations, where the biotic phase is represented by computational particles (parcels), provide information on environmental gradients inside bioreactors from the microbial perspective. Such information is highly relevant for reactor scale-down and process optimization. One of the major challenges is the computational intensity of CFD simulations, especially when resolution of dynamics in the flowfield is required. Lattice-Boltzmann large-eddy simulations (LB-LES) form a very promising approach for simulating accurate, dynamic flowfields in stirred reactors, at strongly reduced computation times compared to finite volume approaches. In this work, the performance of LB-LES in resolving substrate gradients in large-scale bioreactors is explored, combined with the inclusion of a Lagrangian biotic phase to provide the microbial perspective. In addition, the hydrodynamic performance of the simulations is confirmed by verification of hydrodynamic characteristics (radial velocity, turbulent kinetic energy, energy dissipation) in the impeller discharge stream of a 29 cm diameter stirred tank. The results are compared with prior finite volume simulation results, both in terms of hydrodynamic and biokinetic observations, and time requirements.

Blending and cavern formation within non-Newtonian fluids in stirred tanks: Application to nuclear waste fluid processing

“…Repeated studies show that LB algorithms on GPUs run multiple orders-of-magnitude faster than traditional finite volume method (FVM)-based approaches. (Haringa, 2022) (Yang, 2018) Such a large step-change in performance motived us to investigate the appropriateness of these algorithms for non-Newtonian fluids. As part of this theoretical introduction, we validate predictions from the model against exact solutions to the Navier-Stokes equation and measured impeller power number data.…”

Multi‐impeller mixing performance prediction in stirred tanks using mean age theory approach

“…Over the past decades, Lattice Boltzmann (LB) method has challenged finite volume methods regarding speed and accuracy, 21 and the LB method can also provide promising mixing results 22 . In recent work, Haringa 23 proposed that the LB‐LES method can identify mixing characteristics in a bioreactor with computation times similar to those of the steady‐state RANS approach. Furthermore, the computational speed of the LB‐LES simulation by using a Graphics Process Unit (GPU) of a single graphics card in a desktop computer was 1500‐fold faster than that of the unsteady RANS approach on a 16 CPU cores workstation 24 .…”

“…Furthermore, the computational speed of the LB‐LES simulation by using a Graphics Process Unit (GPU) of a single graphics card in a desktop computer was 1500‐fold faster than that of the unsteady RANS approach on a 16 CPU cores workstation 24 . But they also mentioned the limitation of LB‐LES in applied research and industry, which might be due to the lack of commercial/open‐source LB codes, etc 23 …”

“…24 But they also mentioned the limitation of LB-LES in applied research and industry, which might be due to the lack of commercial/open-source LB codes, etc. 23 Recently, Liu 25 applied the steady-state RANS-based mean age theory approach for mixing time prediction in a stirred tank equipped with a single Pitched-blade impeller. Mean age theory is based on the residence time distribution and the mean age is the average time for all materials to pass through a particular location in a continuous system.…”

Modeling multiphase fluid flow, mass transfer, and chemical reactions in bioreactors using large‐eddy simulation

“…Furthermore, a majority of conventional and commercial CFD solvers present limited compatibility with high-performance computing architectures such as Graphics Processing Units (GPUs) [7] thereby, severely restricting their applicability to bioreactor simulations involving transient computations with prolonged time-scales. Alternatively, the lattice-Boltzmann method (LBM) has high parallelism and explicit time-marching characteristics, rendering it to be a suitable tool for transient computations [8].…”